Minimum norm least squares solutionPreconditionerFor the singular, non-Hermitian, and positive semi-definite system of linear equations Ax = b, we introduce a kind of preconditioners for the preconditioned Hermitian and skew-Hermitian splitting (PHSS) iteration method. Then, the iteration sequence ...
If the poles of the system are fixed, then the problem reduces to a linear least-squares problem in two possible ways: by multiplying out the ... J. Van Deun,A Bultheel - 《Journal of Computational & Applied Mathematics》 被引量: 7发表: 2004年 Numerical Methods for Engineers and Scienti...
Noun1.method of least squares- a method of fitting a curve to data points so as to minimize the sum of the squares of the distances of the points from the curve least squares statistics- a branch of applied mathematics concerned with the collection and interpretation of quantitative data and...
In theory, a solution to the ... M Pnar 被引量: 0发表: 2006年 On the Solution of the Tikhonov Regularization of the Total Least Squares Problem Total least squares (TLS) is a method for treating an overdetermined system of linear equations ${\\bf A} {\\bf x} \\approx {\\bf b...
This paper puts forward a row action method for the least-squares solution of the overdetermined linear eqations, proves its convergence, and probes into its accelaration techniques 关键词: Overdetermined linear equations Least-squares solution Minimum least-squares solution Row action method Convergence...
Note: If a linear system has a unique solution, then the least squares solution will be equal to that unique solution. For example, you could design a model to try to predict car prices. For that, you could collect some real-world data, including the car price and some other features ...
Fortunately, when the relative orientation between the two input surfaces is small, we can approximate the nonlinear optimization problem with a linear least-squares one that can be solved more efficiently. We detail the derivation of a linear system whose least-squares solution is a good ...
(λ) to denote its (global) optimal solution. Since the ℓ 1 term promotes sparse solutions, we also refer problem (1) as the sparse least-squares problem. The ℓ 1 -LS problem has important applications in machine learning, signal processing, and statistics; see, e.g., [Tib96, CDS...
The method is based on the newly proposed concept of SOCCs which, for the first time, reveals an important relationship of the OLS based solution to a least-squares problem with the multiple correlation coefficient and the canonical correlation coefficient. Utilising the relationships, the OLS ...
1、最小二乘法(Least squares method)The small square method (also known as the least square method) is a mathematical optimization technique. It matches the best function of finding the data by minimizing the squared error.Using the least square method, the unknown data can be obtained easily,...